Fæhða Gemyndig: Hostile Acts vs. Enmity
Bibliographic record
Abstract
In 1970 Fred C. Robinson warned of uncritical acceptance of dictionary definitions, asserting that definitions, if assigned incorrectly, “can fix the critical interpretation of a passage in a permanent course of error ” (1970: 99–100). Just such a course of error has happened with the Old English word fǣhþ, commonly defined as “feud, state of feuding, enmity, hostility; hostile act ” (DOE 2007: s.v. fǣhþ, fǣhþu). This traditional definition of fǣhþ is fraught with anachronistic connotations and unquestioned assumptions that unduly influence modern readings of Old English texts. In some instances these connotations and assumptions have only a subtle effect on our readings of a text, but in others they can significantly alter how we read the passages in which the word appears. A systematic examination of how fǣhþ is used in context shows that the word more commonly refers to the final element in Toronto’s Dictionary of Old English’s definition: a “hostile act ” or crime, especially homicide. In poetry, the word is used more expansively, encompassing a spectrum of injuries and bad behaviors, from original sin to boastful boorishness. Yet even in poetry the word is most often used to reference a killing. A second sense of fǣhþ that emerges is the retribution inflicted for such an offense, often collocated with
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".